Junior Research Fellows

Ms Kausani holds an MSc by Research in Biotechnology from the Symbiosis School of Biological Sciences (SSBS), Symbiosis International University (2024). Currently, she is a full-time PhD student and a JRF under the guidance of Dr Bhushan Borotikar at SCMIA. Her research interests focus on designing functional nanomaterials and dual-mode biosensing systems for brain tumor detection towards intraoperative precision neurosurgical oncology.

Ms. Himanshi holds an MSc in Data Science and Advanced Computing from the University of Reading, an MTech in Nanoscience and Nanotechnology from Panjab University and a BTech in Computer Science and Engineering, and has built strong skills in machine learning, signal processing, and biomedical imaging. She is currently employed on the DST-PURSE project, where she works on AI-driven healthcare analytics, designing data pipelines and handling complex multimodal datasets.

Mr. Yashraj holds MSc in Biomedical Engineering from Newcastle University, UK (2024). Currently, he is fulltime PhD student and an JRF at SCMIA. He is working in medical imaging analysis (CT, MRI) using machine learning and deep learning models to support advancements in healthcare technology.

Mr. Bikash is currently serving as a Junior Research Fellow at the Symbiosis Centre for Medical Image Analysis (SCMIA), Pune. His work focuses on developing statistical and AI models for personalized detection of cognitive fatigue and digital overuse using EEG signals in young adults, integrating advanced analytics with neurocognitive health research.

He previously worked as a Research Intern at the Indian Council of Social Science Research (ICSSR), New Delhi, and completed an academic internship at the International Institute for Population Sciences (IIPS), Mumbai.

He holds a B.Sc. in Statistics from Fakir Mohan University, Odisha, and an M.Sc. in Statistics from Sambalpur University, Odisha. His research interests span biostatistics, cognitive health analytics, population health, and applied artificial intelligence in medical research.

Bikash has hands-on experience working with large-scale survey datasets such as NFHS and LASI, along with expertise in survey design, statistical modeling, and health data analysis. He is proficient in R, SPSS, STATA, C programming, and LaTeX, with growing engagement in EEG-based cognitive modeling and AI-supported healthcare research.

Kaustubh Kulkarni received the bachelor’s degree in engineering from Mumbai University, and the MSc degree from Auburn University. He worked at Siemens Research Labs in India and the USA. He was a researcher INRIA, Grenoble, France and Computer Vision Center, Universitat Aut onoma de Barcelona. Following which he worked on the problems of designing speech recognition engines on highly noisy speech data for defence applications. He has experience working in medical image analysis, action recognition, speech recognition, and emotion recognition. At SCMIA, he is working on learning multimodal deep learning models for early detection of Alzheimer's disease.

Dr Arunprasad.V.K has done Master of Science (M.Sc) in Human Anatomy from Amrita School of Medicine, Kerala. He is a passionate anatomist with key Interest in Radiological and Developmental Anatomy, with a special focus on fetal autopsy and cross-sectional imaging. He is currently pursuing Ph.D. from SCMIA under the Mentorship of Dr Bhushan Borotikar. His Ph.D research focuses on understanding the neuroimaging patterns in children with cerebral palsy- diagnosis and treatment.

Research Interests & Expertise:

Contact:
Email: arunprasad@smcw.siu.edu.in

Research Profiles:

• [Google Scholar]
   
• [ResearcherID]
   
• [ORCID ID]
   
• [Vidwan]

Dhanashri Joshi has a Master degree in Computer Engineering from Pune Institute of Computer Technology, Pune. Also she has Gate qualified. Currently, she is a full-time Ph.D. student an JRF at SCMIA. Her work focuses on medical image analysis using Machine Learning and Deep Learning techniques.

Contact:
Email: dhanashri.joshi@scmia.edu.in

Surabhi Thatte has a M.Tech in Information Technology from Guru Gobind Singh Indra Prastha University, Delhi. Also she has qualified UGC NET in June 2012 and GATE in 2010. Currently, she is a Part-time Ph.D. students at SCMIA. Her work focuses on medical image analysis using Machine Learning and Deep Learning techniques.

Contact:
Email: surabhi.thatte.phd2020@sitpune.edu.in

Mr. Prabhat working on the SERB-funded project “Multi-domain and Multi-scale Virtual Statistical Musculoskeletal Model” since April 2025. He holds an MSc in Artificial Intelligence from the University of Aberdeen, UK (2023), and a BCA from Symbiosis International University (2021). Previously, he was a Design Engineer at Panacea Medical Technology, where he led key developments in a Surface Guided Radiotherapy system. His work spanned stereo vision, point cloud processing, RGB-D fusion, CUDA-accelerated optimization, machine learning, and 3D registration—significantly improving clinical alignment accuracy and performance. His expertise includes CUDA, TensorFlow, OpenCV, Open3D, Python, and deep learning. Prabhat is passionate about medical image processing, multi-view geometry, statistical shape modeling, and GPU-based computing, and he actively shares knowledge via blogs and open-source AI community.

Ms. Shikha is currently working under Dr. Bhushan Borotikar on the SERB-funded project "Multi-domain and Multi-Scale Virtual Statistical Musculoskeletal Model" at SCMIA. She completed her M.Tech. in Computational Biology from IIIT-Delhi (2024) and her B.Tech. in Biotechnology from AKTU (2021).

Previously, she worked as a Project Assistant at IBAB, Bengaluru, focusing on probabilistic modelling using communication channel algorithms. Her master's thesis involved developing a graph-theory-based scoring function for protein-ligand binding affinity, while her bachelor's thesis focused on screening onion- and garlic-derived compounds against COVID-19 Mpro protein.

Currently, she is working with medical imaging data, including MRI and CT scans, to support advancements in musculoskeletal modeling.